Recently there has been an effort in the community to phrase any NLP task in a unified fashion as a sequence to sequence task (seq-2-seq).
Examples of this are phrasing multiple tasks as question answering [1]}, the T5 model [2]}, the focus on prompt priming [3]}, [4]} and GPT3 and its few-shot learning approach for multiple tasks [5]}.
There is also a separate line of research that aims to inject commonsense or factual knowledge into language models by training them on data derived from knowledge graphs.
An example of this is injecting numerical reasoning skills [6]} or commonsense knowledge [7]}.
This is orthogonal to retrieval-based approaches (such as dialogue generation with commonsense knowledge base access by [8]}) on which we focus in this paper.
